AI Communications - Volume 29, issue 4

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ISSN 0921-7126 (P)
ISSN 1875-8452 (E)

Impact Factor 2018: 0.461

AI Communications is a journal on Artificial Intelligence (AI) which has a close relationship to ECCAI (the European Coordinating Committee for Artificial Intelligence). It covers the whole AI community: scientific institutions as well as commercial and industrial companies.

AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news. The Editorial and Advisory Board is appointed by the Editor-in-Chief.

Abstract: Bank failure prediction is an important study for regulators in the banking industry because the failure of a bank leads to devastating consequences. If bank failures are correctly predicted, early warnings can be sent to the responsible authorities for precaution purposes. Therefore, a reliable bank failure prediction or early warning system is invaluable to avoid adverse repercussion effects on other banks and to prevent drastic confidence losses in the society. In this paper, we propose a novel self-organizing neural fuzzy inference system, which functions as an early warning system of bank failures. The system performs accurately based on the auto-generated…fuzzy inference rule base. More importantly, the simplified rule base possesses a high level of interpretability, which makes it much easier for human users to comprehend. Three sets of experiments are conducted on a publicly available database, which consists of 3635 United States banks observed over a 21-year period. The experimental results of our proposed model are encouraging in terms of both accuracy and interpretability when benchmarked against other prediction models.
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Abstract: In this research work, a simple Electroencephalogram (EEG) based imagery vocabulary classification system has been developed for the Differentially Enabled (DE) communities, to communicate their needs with the outside world. The proposed communication system consists of a simple data acquisition protocol, which includes the basic needs of DE patients in their daily life, such as Food, Water, Toilet, Help, Aircon, Tv and Relax. The EEG signals for each task are recorded from ten subjects using a standard wireless EEG amplifier from eight different electrode positions. The recorded brain wave patterns are pre-processed and segmented into four frequency bands, namely Delta…(δ ), Theta (θ ), Alpha (α ) and Beta (β ). A simple feature extraction technique using cross-correlation (r ) estimation has been proposed to extract the coefficients between any two frequency bands. Similarly, six permutation sets of four frequency bands for each electrode position are framed and the statistical features such as minimum (min), maximum (max), mean (μ ), standard deviation (σ ), skewness (G ) and kurtosis (K ) are computed to form the feature sets. The extracted feature sets are classified using three different supervised non-parametric classification methods, namely, k -Nearest Neighbor (k -NN), Multilayer Neural Network (MLNN) and Probabilistic Neural Network (PNN). Further, the classification models are compared and from the results it is observed that the k -NN classifier hits the highest classification accuracy of 90.24% using max feature set.
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Abstract: In this paper we consider several variants of the problem of sorting integer permutations with a minimum number of moves, a task with many potential applications ranging from computational biology to logistics. Each problem is formulated as a heuristic search problem, where different variants induce different sets of allowed moves within the search tree. Due to the intrinsic nature of this category of problems, which in many cases present a very large branching factor, classic unidirectional heuristic search algorithms such as A∗ and IDA∗ quickly become inefficient or even infeasible as the problem dimension grows. Therefore, more sophisticated…algorithms are needed. To this aim, we propose to combine two recent paradigms which have been employed in difficult heuristic search problems showing good performance: enhanced partial expansion (EPE) and efficient single-frontier bidirectional search (eSBS). We propose a new class of algorithms combining the benefits of EPE and eSBS, named efficient Single-frontier Bidirectional Search with Enhanced Partial Expansion (eSBS-EPE). We then present an experimental evaluation that shows that eSBS-EPE is a very effective approach for this family of problems, often outperforming previous methods on large-size instances. With the new eSBS-EPE class of methods we were able to push the limit and solve the largest size instances of some of the problem domains (the pancake and the burnt pancake puzzles). This novel search paradigm hence provides a very promising framework also for other domains.
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Abstract: In this paper we describe a complete quantum computational model of computation that is based on the production system. The presented description of the model does not require any quantum computation background. By doing so, the reason that quantum computation may be important for pure symbolical artificial intelligence may become clear. The feature universal quantum computer based on this idea will involve programs that are related to the classical artificial intelligence programming languages such as OPS5.

Abstract: Genetic algorithms are commonly used in many types of applications. Yet they suffer from longer execution time and premature convergence. To improve both these factors, the research work proposes two new procedural modifications in the basic genetic algorithm procedure and a new population initialization mechanism. The proposed algorithms are implemented in two types of real world problems of different sizes and the results confirm the superiority of the algorithms over existing ones both in terms of execution time and optimality.

Abstract: We propose a novel methodology to infer gene association networks from gene expression profiles (microarray data) based on the application of model tree. We first build a regression tree for each gene and second, we build a graph from all the linear relationships among output and input genes taking into account whether the pair of genes is statistically significant. Then, we apply a statistical procedure to control the false discovery rate. Part of this methodology is a key component in a prediction-based method for a cardiovascular problem based on the discovery of clinically relevant transcriptional association networks. The aim of…this second method is to apply the information encoded in gene networks for prognostic purpose which is one of the crucial objectives of systems biomedicine.
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